The ANR project Huge Digital Worlds has been accepted and funded by the Agence Nationale pour la Recherche.

In this project, we address the generation, processing and rendering of huge realistic scenes
using an original and unique approach: program-based generative content production, also called
procedural modeling. Instead of explicit representations (triangles and pixels), procedural models
generate content on demand by means of programs depending on finite sets of parameters.

Digital landscape
realism often comes from the multitude of details that are hard to model such as fallen leaves,
rock piles or entangled fallen branches. In this article, we present a method for augmenting
natural scenes with a huge amount of details such as grass tufts, stones, leaves or twigs.
Our approach takes advantage of the observation that those details can be approximated by
replications of a few similar objects and therefore relies on mass-instancing. We propose
an original structure, the Ghost Tile, that stores a huge number of overlapping candidate
objects in a tile, along with a pre-computed collision graph. Details are created by traversing
the scene with the Ghost Tile and generating instances according to user-defined density fields
that allow to sculpt layers and piles of entangled objects while providing control over their
density and distribution.

Two papers, Sparse representation of terrains for procedural modeling and Large Scale Terrain Generation from Tectonic Uplift and Fluvial Erosion have been accepted at Eurographics.

In the sparse modeling paper, we present a simple and efficient method to represent terrains as elevation functions built from linear combinationsof landform features (atoms). These features can be extracted either from real world data-sets or procedural primitives, orfrom any combination of multiple terrain models. Our approach consists in representing the elevation function as a sparsecombination of primitives, a concept which we call Sparse Construction Tree, which blends the different landform featuresstored in a dictionary. The sparse representation allows us to represent complex terrains using combinations of atoms from asmall dictionary, yielding a powerful and compact terrain representation and synthesis tool. Moreover, we present a methodfor automatically learning the dictionary and generating the Sparse Construction Tree model. We demonstrate the efficiency ofour method in several applications: inverse procedural modeling of terrains, terrain amplification and synthesis from a coarsesketch.

We propose a novel approach for authoring large scenes with automatic enhancement of objects to create geometric
decoration details such as snow cover, icicles, fallen leaves, grass tufts, or even trash. We introduce
environmental objects that extend an input object geometry with a set of procedural effects that defines how the
object reacts to the environment, and by a set of scalar fields that defines the influence of the object over of the
environment. The user controls the scene by modifying environmental variables, such as temperature or humidity
fields. The scene definition is hierarchical: objects can be grouped and their behaviors can be set at each level of
the hierarchy. Our per object definition allows us to optimize and accelerate the effects computation, which also
enables us to generate large scenes with many geometric details at a very high level of detail. In our implementation,
a complex urban scene of ten-thousand square meters, represented with details of less than one centimeter,
can be locally modified and entirely re-generated in a few seconds.

We introduce a compact hierarchical procedural model that combines feature-based primitives to describe complex
terrains with varying level of detail. Our model is inspired by skeletal implicit surfaces and defines the terrain
elevation function by using a construction tree. Leaves represent terrain features and they are generic parameterized
skeletal primitives such as mountains, ridges, valleys, rivers, lakes, or roads. Inner nodes combine the leaves
and subtrees by carving, blending, or warping operators. The elevation of the terrain at a given point is evaluated
by traversing the tree and by combining the contributions of the primitives. The definition of the tree leaves and
operators guarantees that the resulting elevation function follows the Lipschitz property which speeds up sphere
tracing and adaptive tessellation algorithms used to render the terrain. Our model is compact and allows for the
creation of large terrains with a high level of detail using a reduced set of primitives. We show the creation of different
kinds of landscapes and demonstrate that our model allows to efficiently control the shape and distribution
of landform features.

About

Eric Galin is Professor of Computer Science at University Lyon 1, France.
He received an engeneering degree from Ecole Centrale de Lyon in 1993 and a PhD in
Computer Science from Université Lyon 1 in 1997.

His research interests cover procedural modeling of virtual worlds, simulating natural
phenomena and modeling with implicit surfaces. For the past decade, he contributed
to a number of high level procedural methods for modeling terrains, generating road
networks and villages, generating river networks and simulating the mutual influence
between ecosystems and terrains.

Eric Galin served in many program committees such as SMI, Eurographics and the series
of Eurographics Workshop on Natural Phenomena.
He was the co-chair of Eurographics 2017 conference in Lyon. In France,
he participated to the creation of the French Chapter of Eurographics in 2004 and belongs
to the CA. From 2008 to 2013, he was the head of Gamagora, a high level education program
combining Level Design, Graphics Design and Computer Game Programming tracks.